Implementation of Multiview Deep Learning Algorithm using LSTM-CNN for Fraudulent Review Detection

Implementation of Multiview Deep Learning Algorithm using LSTM-CNN for Fraudulent Review Detection

Priyanka Gupta, Abhishikth Xavier Kapudasi, Rakesh Eemani, Kaza Chandu Koundinya, Sujith Dugyala

School of Computer Science and Engineering

Lovely Professional University Punjab, India

Abstract - In this project, we explored the task of detecting fraudulent reviews in text data using various machine learning algorithms. The dataset consisted of reviews categorized into two labels: 'CG' (genuine reviews) and 'OR' (potentially fraudulent reviews). We began by preprocessing the text data, which involved tasks such as removing punctuation, stop words, and applying stemming or lemmatization techniques. After preprocessing, we utilized different machine learning models including Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Support Vector Machines (SVM), Multinomial Naive Bayes and LSTM-CNN model to classify the reviews. Overall, our study showcases the effectiveness of machine learning, Deep learning algorithms in identifying fraudulent reviews from textual data. The findings provide valuable insights for applications in e-commerce platforms and online review systems to enhance the reliability and trustworthiness of user-generated content.

Keywords: Fraudulent reviews, Text classification, Machine Learning, Preprocessing, Logistic Regression, Support Vector Machines, Evaluation metrics.